City Research Online

Abstract

We introduce a method for transferring style from the logos of heavy metal bands onto corporate logos using a VGG16 network. We establish the contribution of different layers and loss coefficients to the learning of style, minimization of artefacts and maintenance of readability of corporate logos. We find layers and loss coefficients that produce a good tradeoff between heavy metal style and corporate logo readability. This is the first step both towards sparse font style transfer and corporate logo decoration using generative networks. Heavy metal and corporate logos are very different artistically, in the way they emphasize emotions and readability, therefore training a model to fuse the two is an interesting problem.

Publication Type:	Conference or Workshop Item (Paper)
Publisher Keywords:	Neural Font Style Transfer, Generative Networks
Subjects:	H Social Sciences > HD Industries. Land use. Labor N Fine Arts > NC Drawing Design Illustration Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Departments:	School of Science & Technology > Computer Science

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